What is uncertainty?

Show the data (1/4)

election <- read_csv(here::here("session1/data/election2019.csv"),
  skip = 1,
  col_types = cols(
    .default = col_character(),
    OrdinaryVotes = col_double(),
    AbsentVotes = col_double(),
    ProvisionalVotes = col_double(),
    PrePollVotes = col_double(),
    PostalVotes = col_double(),
    TotalVotes = col_double(),
    Swing = col_double()
  )
)
e_grn <- election |>
  group_by(DivisionID) |>
  summarise(
    DivisionNm = unique(DivisionNm),
    State = unique(StateAb),
    votes_GRN = TotalVotes[which(PartyAb == "GRN")],
    votes_total = sum(TotalVotes)
  ) |>
  mutate(perc_GRN = votes_GRN / votes_total * 100)

e_grn |>
  mutate(State = fct_reorder(State, perc_GRN)) |>
  ggplot(aes(x=perc_GRN, y=State)) +
    geom_quasirandom(groupOnX = FALSE, varwidth = TRUE) +
    labs(
      x = "First preference votes %",
      y = ""
    ) +
  xlim(c(0,50))

Show the data (2/4)

Show the data (3/4)

e_grn |>
  mutate(State = fct_reorder(State, perc_GRN)) |>
  ggplot(aes(x=perc_GRN, y=State)) +
    geom_boxplot(varwidth = TRUE) +
    labs(
      x = "First preference votes %",
      y = ""
    ) +
  xlim(c(0,50))

e_grn |>
  mutate(State = fct_reorder(State, perc_GRN)) |>
  ggplot(aes(x=perc_GRN, y=State)) +
    geom_violin(draw_quantiles = c(0.25, 0.5, 0.75),
      fill="#006dae", alpha=0.5) +
    labs(
      x = "First preference votes %",
      y = ""
    ) +
  xlim(c(0,50))

oz_states <- ozmaps::ozmap_states %>% filter(NAME != "Other Territories")
oz_votes <- rmapshaper::ms_simplify(ozmaps::abs_ced)
oz_votes_grn <- full_join(oz_votes, e_grn, by=c("NAME"="DivisionNm"))

ggplot(oz_votes_grn, aes(fill=perc_GRN)) +
  geom_sf(colour="white") +
  scale_fill_viridis_c(direction=-1, trans = "log", 
    guide = "colourbar", 
    labels = scales::label_number(accuracy = 0.1)) +
  theme_map() +
  theme(legend.position = "right", 
    legend.title = element_blank())

Show the data (4/4)

Terminology

Example: distributions

load("data/melbtemp.rda")
melbtemp_2019 <- melbtemp |>
  filter(year == 2019)
  
d1 <- ggplot(melbtemp_2019, aes(x=month, y=temp)) +
  geom_quasirandom() + 
  stat_summary(geom="point", fun="median", 
    colour="red", size=3) +
  xlab("") + ylab("Temp (C)") +
  ggtitle("A. ggbeeswarm::geom_quasirandom")
  
library(ggforce)
d2 <- ggplot(melbtemp_2019, aes(x=month, y=temp)) +
  geom_violin(fill = "#6F7C4D", colour=NA, alpha=0.7) +
  geom_sina() +
  xlab("") + ylab("Temp (C)") +
  ggtitle("B. geom_violin + ggforce::geom_sina")

library(ggridges)
d3 <- ggplot(melbtemp_2019, aes(x=temp, y=month)) +
  geom_density_ridges(scale = 1.5, 
                      quantile_lines = TRUE,
                      quantiles = 2,
                      fill = "#6F7C4D") +
  xlab("Temp (C)") + ylab("") + 
  theme_ridges() +
  ggtitle("C. ggridges::geom_density_ridges")

library(ggdist)
d4 <- ggplot(melbtemp_2019, aes(x=temp, y=month)) +
  stat_halfeye(fill="#6F7C4D", alpha=0.7) +
  geom_point(pch = "|", size = 2,
    position = position_nudge(y = -.15)) +
  xlab("Temp (C)") + ylab("") +
  ggtitle("D. ggdist::stat_halfeye")

lout <- c(area(1,2),
          area(3),
          area(4))
lout <- "
AACD
BBCD
"
d1 + d2 + d3 + d4 + plot_layout(design=lout)

Your turn

1. Decide on the appropriate information about the variability that needs to be included in the display.
2. Play with different options on your choice of display to make various displays. Aim to have three different designs.
3. Is there a winner, or several roughly equally good displays?

Why “signal suppression”? (1/4)

Regression

Why “signal suppression”? (2/4)

Longitudinal data

Why “signal suppression”? (3/4)

Forecasts

Why “signal suppression”? (4/4)

Map

Why can’t we just treat uncertainty as another variable? * Uncertainty is not just another variable… * It presents an interesting perceptual problem * Two sides of the same coin * Usually do not want variables to interfere with each other * In uncertainty visualisation, the opposite is true * This is the core of the signal suppression approach we implement

from the ggdibbler notes the conditional line plot. All the examples are from time series packages, because I spent a bit of time looking for the one that returned a distributional forecast so I could use it in an example

Common ways we express uncertainty in R and when you would see them out in the wild? - Distributional (vectorised distributions that are wrappers on the d, p, q, r functions) - e.g. when you get it as output - fable, some Bayes stuff (e.g. posterior) - Base R (d, p, q, r functions) - Confidence intervals - hi low variables in the forecast packages - Bootstrapping (?)

Evaluation criteria - Uncertainty visualisation should:

1. Reinforce justified signals
2. Hide signals that are just noise Similar logic to a hypothesis test (but not as binary, and without set thing you are checking for)

Accessibility considerations

• Colour
  • 2D palette is harder to read as colour is not a simple 3D space. Mapping a variable to saturation hurts accessibility. (Maybe specifically mention this in section 2?. I am not sure of other accessibility issues other than colour issues (and maybe size?).

Miscellaneous

• bag plot
• model only (regression, wages example) vs adding data
• ggdist
• tidyindex
• animation over space

Where to learn more

• Spiegelhalter, D. (2017), Risk and uncertainty communication